xOpat: eXplainable Open Pathology Analysis Tool

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Authors

HORÁK Jiří FURMANOVÁ Katarína KOZLÍKOVÁ Barbora BRÁZDIL Tomáš HOLUB Petr KAČENGA Martin GALLO Matej NENUTIL Rudolf BYŠKA Jan RUSŇÁK Vít

Year of publication 2023
Type Article in Periodical
Magazine / Source COMPUTER GRAPHICS FORUM
MU Faculty or unit

Faculty of Informatics

Citation
Web https://diglib.eg.org/handle/10.1111/cgf14812
Doi http://dx.doi.org/10.1111/cgf.14812
Keywords Medical Imaging; Scientific Visualization; Open Pathology; Toolkit; artificial intelligence; Visual Analysis; AI explainability; GPU Rendering;
Description Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hinders the adoption of new methods and communication between the researchers and clinicians. The paper presents xOpat, an open-source, browser- based WSI viewer that addresses these problems. xOpat supports various data sources, such as tissue images, pathologists’ annotations, or additional data produced by AI models. Furthermore, it provides efficient rendering of multiple data layers, their visual representations, and tools for annotating and presenting findings. Thanks to its modular, protocol-agnostic, and extensible architecture, xOpat can be easily integrated into different environments and thus helps to bridge the gap between research and clinical practice. To demonstrate the utility of xOpat, we present three case studies, one conducted with a developer of AI algorithms for image segmentation and two with a research pathologist.
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